ARCHIE++ : A Cloud-Enabled Framework for Conducting AR System Testing in the Wild
| Autor: | Sarah M. Lehman, Semir Elezovikj, Haibin Ling, Chiu C. Tan |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | IEEE Transactions on Visualization and Computer Graphics. 29:2102-2116 |
| ISSN: | 2160-9306 1077-2626 |
| DOI: | 10.1109/tvcg.2022.3141029 |
| Popis: | In this paper, we present ARCHIE++, a testing framework for conducting AR system testing and collecting user feedback in the wild. We begin by presenting a set of current trends in performing human testing of AR systems, identified by reviewing a selection of recent work from leading conferences in mixed reality, human factors, and mobile and pervasive systems. From the trends, we identify a set of challenges to be faced when attempting to adopt these practices to testing in the wild. These challenges are used to inform the design of our framework, which provides a cloud-enabled and device-agnostic way for AR systems developers to improve their knowledge of environmental conditions and to support scalability and reproducibility when testing in the wild. We then present a series of case studies demonstrating how ARCHIE++ can be used to support a range of AR testing scenarios, and demonstrate the limited overhead of the framework through a series of evaluations. We close with additional discussion on the design and utility of ARCHIE++ under various edge conditions. |
| Databáze: | OpenAIRE |
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